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Abstract:

The solution to the static appointment problem is adaptive appointment
scheduling, where the system can estimate the arrival time of a truck
with the given information like ETA (Estimated Time of arrival) of truck
and truck route traffic information, and it can adjust the appointments
accordingly, to manage terminal resources efficiently and minimize the
congestion. In addition, we present a new method and system to calculate
and present options when planning to move or transfer from point A to
point B. We are introducing a new concept where user have more defining
measurable factors in route selection, with option to further optimize
the result based on user preference. Other examples and methods are also
given.

Claims:

1. A method for presenting entries for schedules for transportation to a
user, said method comprising: first input module receiving trucking data;
second input module receiving terminal or port data; a processor
calculating estimated time of arrival; third input module receiving
global positioning system signal, radio frequency identification signal,
or phone call data; updating said estimated time of arrival using said
global positioning system signal, radio frequency identification signal,
or phone call data; a graphical user interface mapping a first truck with
said updated estimated time of arrival; said graphical user interface
prompting said user to filter entries according to terminal location and
appointment date; said graphical user interface summarizing truck
arrivals; said processor receiving one or more rules from a policy
module; adjusting said truck arrivals based on said one or more rules;
displaying said adjusted truck arrivals.

2. The method for presenting entries for schedules for transportation to
a user as recited in claim 1, wherein said one or more rules are based on
number of changes.

3. The method for presenting entries for schedules for transportation to
a user as recited in claim 1, wherein said one or more rules are based on
changes.

4. The method for presenting entries for schedules for transportation to
a user as recited in claim 1, wherein said one or more rules are based on
waiting time.

5. The method for presenting entries for schedules for transportation to
a user as recited in claim 1, wherein said one or more rules are based on
cost.

6. The method for presenting entries for schedules for transportation to
a user as recited in claim 1, wherein said one or more rules are based on
order backlog.

7. The method for presenting entries for schedules for transportation to
a user as recited in claim 1, wherein said one or more rules are based on
total mileage.

8. The method for presenting entries for schedules for transportation to
a user as recited in claim 1, wherein said one or more rules are based on
gas expense.

9. The method for presenting entries for schedules for transportation to
a user as recited in claim 1, wherein said one or more rules are based on
inventory.

10. The method for presenting entries for schedules for transportation to
a user as recited in claim 1, wherein said one or more rules are based on
truck traffic.

11. The method for presenting entries for schedules for transportation to
a user as recited in claim 1, said method comprising: distinguishing and
classifying late arrivals.

12. The method for presenting entries for schedules for transportation to
a user as recited in claim 1, said method comprising: distinguishing and
classifying early arrivals.

13. The method for presenting entries for schedules for transportation to
a user as recited in claim 1, said method comprising: distinguishing and
classifying on-time arrivals.

14. The method for presenting entries for schedules for transportation to
a user as recited in claim 1, wherein said one or more rules are static.

15. The method for presenting entries for schedules for transportation to
a user as recited in claim 1, wherein said one or more rules are dynamic.

16. The method for presenting entries for schedules for transportation to
a user as recited in claim 1, wherein said one or more rules are changed
by said user.

17. The method for presenting entries for schedules for transportation to
a user as recited in claim 1, wherein said one or more rules are changed
by an administrator.

18. The method for presenting entries for schedules for transportation to
a user as recited in claim 1, wherein said one or more rules are changed
by said processor.

19. The method for presenting entries for schedules for transportation to
a user as recited in claim 1, wherein said one or more rules are based on
said trucking data;

20. The method for presenting entries for schedules for transportation to
a user as recited in claim 1, wherein said one or more rules are based on
said terminal or port data.

Description:

RELATED APPLICATION

[0001] This application is a continuation-in-part (CIP) of another
co-pending U.S. application Ser. No. 13/411,578, titled "Method and
System for Calculating and Presenting Options for Planning
Transportation", filed 4 Mar. 2012, with the same assignee, NTELX Inc.
The teachings and all specification of Ser. No. 13/411,578 are
incorporated herein by reference.

BACKGROUND

[0002] The port terminals create appointment slots with the specifications
like time, duration and capacity of the slot. The trucking companies
check the available slots and take an appointment as per the need basis.
The booking for the slot is closed after specific interval of time.
Further, the terminal operator manages the terminal resources according
to the appointments taken.

[0003] If the truck is not able to arrive on schedule, as per the
appointment time, due to some inevitable reasons like congestion,
breakdown, etc., the truck arrives at a later time, which is an
appointment time for some other truck. This causes congestion on the
port, or waiting time, even for on-time arrived trucks, and thus, results
in the mismanagement of the terminal resources.

[0004] In addition, we present a new way to calculate and present routing
options when you are travelling from point A to point B, or moving
objects between 2 locations. The main problem that the travelers
currently face when selecting trip options is that there is no holistic
way to evaluate all aspects of an option. We elucidate this problem using
some examples. For example, it is possible that the cheapest trip option
takes about 3 times as long as an option that only costs a small fraction
more. Similarly, the shortest possible option may cost 10 times as much
as another option that takes fractionally longer time. Also, it is
possible that an option that is shortest in time, as well as cheapest,
uses an airline that has very low customer satisfaction. As another
example, when shipping a container between two cities, it is possible
that the cheapest and the fastest service is provided by a trucking
company that has the largest (worst) environmental impact. The user
generally would like a way to consolidate all options of a transportation
option (whether for passenger travel or for freight transportation), in
order to make a choice that performs reasonably well on multiple aspects.

SUMMARY

[0005] The solution to the static appointment problem is adaptive
appointment scheduling, where the system can estimate the arrival time of
a truck with the given information like ETA (Estimated Time of arrival)
of truck and truck route traffic information, and it can adjust the
appointments accordingly, to manage terminal resources efficiently and
minimize the congestion.

[0006] We will have the following features:

[0007] The route traffic
data, truck location and other information help in estimation of arrival
time information.

[0008] The appointment to arrival time mapping helps
user to visualize the individual truck arrival status at any time.

[0010] Based
on status and summary information, system will adjust appointments, or
user can adjust any appointment manually.

[0011] While adjusting
appointments by the system, different policies/rules are applied.

[0012] In addition, we present a new method and system to calculate and
present options when planning a trip from point A to point B. We are
introducing a new concept where user have more defining measurable
factors in route selection, with option to further optimize the result
based on user preference. The method can be used for passenger trip
planning (for example, when booking a flight) and for freight trip
planning (for example, when shipping an object).

[0013] The method consists of 4 phases. First, we consider all attributes
of each option. Second, we remove all options for which some attributes
do not perform within lower and upper bounds. Third, we run a
multi-criteria optimization to order the options. Finally, the system
presents the options to the user. The user can then select any of the
options. If needed, the user can also modify the configuration options
and restart the process. See FIG. 1 for the process.

[0027]FIG. 14 shows a flow chart of a method of an embodiment of the
invention.

DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] Here is one of the embodiments of the invention:

[0029] Arrival Information:

[0030] The appointment system is provided for the estimated time of
arrival (ETA) of the truck, which helps the system to adjust the
appointment dynamically. ETA is expected to be updated with the help of
real-time information like GPS, RFID, phone calls, etc.

[0031] Appointments Visualizer: Criss-Cross Visualization:

[0032] The UI (user interface) shows the mapping of appointment of a truck
with its ETA. The interface allows user to filter the entries according
to terminal location and appointment date. FIG. 8 shows an example of
such system. Note that 2 of the items in the schedule for times indicated
are highlighted in FIG. 8, for possible changes, in one embodiment.

[0033] Appointments Summary:

[0034] The UI shows a summary of truck arrivals at a specific time slot
for a terminal location. The interface allows user to filter the entries
according to terminal location. FIG. 9 shows an example of such system.

[0035] Appointment adjustment screen:

[0036] Besides the automated appointment adjustments, the system also
provides the option to adjust appointments manually. The option for
manual adjustment is provided on appointments home page, corresponding to
each appointment, and clicking on it takes the user to a new page where
the user is notified about all actions that will be executed as the
result of manual adjustment. FIG. 10 and FIG. 11 show examples of such
system.

[0037] Many policies can be supported:

[0038] We provide the system with different policies, and adaptive
scheduling/adjustments are automatically executed according to the
selected policy. The different policies supported by our system are, as
an example:

[0039] Maximize the number of turns

[0040] Minimize the
waiting time

[0041] Minimize appointment changes

[0042] Custom

[0043]FIG. 12 shows an example of such system. FIG. 13 shows an example
of such method.

[0044] Now, let's look at another embodiment:

[0045] Gathering configuration options: In this setup phase, the system
gathers some configuration options from the user, including the lower and
upper bounds for all attributes, and the options for aggregate objective
function. See FIG. 2 for an embodiment of an interface.

[0046] Considering all aspects of an option: As a first step, we expand
the number of aspects that can be quantified and compared. Commonly use
parameters in a traditional design are transit time and cost.
Additionally, we also consider other options, such as the number of
intermediate stops, quality of those intermediate stops, carrier
reliability, carrier customer satisfaction rating, environmental impact,
and the like. Based on the specific situation, any specific aspect that
has a quantifiable score attached to it can be included as an aspect of
an option. An example list of options with 5 attributes (transit time,
cost, stops, environmental impact, and carrier reliability) is shown
below. It brings multiple dimensions to the selection process.

[0047] Multiple Criteria Optimization: Multiple criteria optimization is
the process of simultaneously optimizing two or more conflicting
objectives subject to certain constraints. Multi criteria optimization
problems can be found in various fields: product and process design,
finance, aircraft design, the oil and gas industry, automobile design, or
wherever optimal decisions need to be taken in the presence of trade-offs
between two or more conflicting objectives.

[0048] In mathematical terms, the multi criteria problem can generally be
written as:

Minx[μ1(x), . . . ,μ2(x)]T

[0049] Such that

[0050] g(x)≦0

[0051] h(x)=0

[0052] x1≦x≦xu

[0053] wherein μi is the i-th objective function, g and h are the
inequality and equality constraints, respectively, and x is the vector of
optimization or decision variables, with x1 and xu denoting the
limits.

[0054] Aggregate objective function: One method for finding a solution to
a multi criteria optimization problem is constructing a single aggregate
objective function (AOF). The basic idea is to combine all of the
objectives into a single objective function, called the AOF, such as
weighted linear sum of the objectives. This objective function is
optimized, subject to technological constraints specifying how much of
one objective must be sacrificed, from any given starting point, in order
to gain a certain amount regarding the other objective. One can use a
matrix, such as 2×2 matrix, for the presentation.

[0055] Linear programming: It is a mathematical method for determining a
way to achieve the best outcome (such as maximum profit or lowest cost)
in a given mathematical model for some list of requirements represented
as linear relationships.

[0056] Canonical form: Linear programs problems can be expressed in
canonical form, in this general form:

[0057] Maximize or Minimize CTx

[0058] Subject to: Ax≦b and 0≦x

[0059] where x represents the vector of variables (to be determined), c
and b are vectors of (known) coefficients, and A is a (known) matrix of
coefficients. The expression to be maximized or minimized is called the
objective function (CTx in this case). The equations of type
(Ax≦b) are the constraints which specify a convex polytope (in
N-dimension) over which the objective function is to be optimized.

[0060] For example, one may want to choose flights of less than 8 hours,
plus price of less than 300 US$.

[0061] Standard Form: Standard form is a form of describing a linear
programming problem. It consists of the following four parts:

[0062] A
linear function to be maximized, e.g.:

[0062] Maxx1,x2f(x1,x2)=c1x1+c2x2

[0063] Problem constraints of the following form, e.g.:

[0063] a11x1+a12x2≦b1

a21x1+a22X2≦b2

a31x1+a32x2≦b3

[0064] Non-negative
variables, e.g.:

[0064] 0≦x1

0≦x2

[0065] Non-negative right-hand side constants:

[0065] 0≦bi, for i=1, 2, 3, . . . .

[0066] The problem is usually expressed in matrix form, and then becomes:

Max{CTx/(0≦Ax≦b) (0≦x)}

[0067] Other forms, such as minimization problems, problems with
constraints on alternative forms, as well as problems involving negative
variables, can always be rewritten into an equivalent problem in the
standard form.

[0068] The following algorithms can be used: Simplex algorithm of Dantzig,
Criss-cross algorithm, Ellipsoid algorithm, Projective algorithm of
Karmarkar, or Path-following algorithms.

[0069] In the following, we show more aspects of a user planning a trip
from New Jersey to Los Angeles. With each aspect having measurable value
attached to it, this new representation of routing option would help user
to have a broader view on available options.

[0070] Removal of Options that do not meet some bounds: In this phase, all
options for which some attributes do not meet some bounds are removed.
For example, it may be that the passenger does not wish to travel on a
flight that has more than 3 intermediate stops, or that the business does
not want to use any trucking company that has a total environmental
impact greater than a preselected value.

[0071] An example algorithm listing that achieves this phase is as
follows:

TABLE-US-00002
For O in List of Options:
For A in List of Attributes:
if [ (O.A < preselectedLowerBound(A) ) or
( O.A > preselecedUpperBound (A) ) ]
Then: Remove O from List of Options &
proceed to the next option

[0072] In route option with different aspects, there is a possibility of
conflicting attributes. For example, keeping the cost low, as well as
transit time low, with no late departures, requires multiple criteria
optimization. Similarly, keeping intermediate stops low or zero and cost
low requires multiple criteria optimization.

[0073] In one embodiment, we do not use linear programming, because
Aggregate Objective Functions can use non-linear equations, as well. In
that respect, linear programming (LP) formulation is a special case of
Aggregate Objective Functions (AOF). LP formulations can typically be
solved faster, but they do not provide adequate flexibility in terms of
options.

[0074] User Interface Options for Configuring Multi-Criteria Optimization:
Tables 2-3 show an example form where user provides information about the
origin, destination and date of travel. Also, user specifies its
preferences (weightage) for the aspects, such as transit time, cost,
carrier reliability, intermediate stops, and the like. User can specify
which aspect is important, not-important, or neutral. In the example,
user specifies transit time and environmental impact as important, cost
and intermediate stops as neutral, and carrier reliability as
not-important.

[0075] Presenting of Options to the User: Tables 2-3 display the routing
option, showing different aspects. The Rank Index column shows the rank
of each particular route option, considering user specified options for
the aggregate objective function. The user interface lists the options in
the descending order of that aggregate objective function. (See Tables
2-3.)

[0076] As shown in Table 2, the first row is check-marked, and the
3rd row is highlighted, as shown in Table 3, below, as the Appendix
to Table 2, e.g. Table 3 being overlapped on Table 2, on screen of the
computer, for the user interface (GUI), originating from the last (on the
right side) column of the 3rd row, from the symbol shown on the
table.

[0077] Please note that, in one embodiment, we have (for the Table 3,
above):

Rank index=Σ(weightage×rating)

[0078] The comparison for cost can be done using absolute values or
relative values, e.g. the lowest number in the table, as 80 US$, as the
baseline. An average number can also be used as the baseline. Then, the
ratio of the cost values is multiplied to weight, for comparison
purposes, for other possibilities, to find the optimum choice(s).

[0079]FIG. 3-6 show multiple systems for embodiments of this invention,
with various components.

[0080] In one embodiment, our system has a central processing unit, along
with multiple storage units, with some user input interface/unit, and
communication units between processing module and other modules. The data
or parameters are stored in memory units, storages, databases, tables,
lists, spreadsheets, physical devices or modules or units, or the like.
The comparisons and calculations are done by a system, processor,
computer, server, computing device, or microprocessor. The modules are
connected through buffers or other memory units, with another processor
directing all the data transfer and actions, as one embodiment. One can
combine processors and memory units, in one or fewer units, if desired,
in another embodiment.

[0081] In one embodiment, we have a method for calculating and presenting
options to a user for planning a trip or transportation, with the
following steps: an aggregation module gathering configuration options; a
construction module receiving all attributes; the construction module
building options, using said all attributes; an evaluator module
determining one or more attributes that do not satisfy one or more
predetermined conditions; and for those one or more attributes, pruning
corresponding options; running multi-criteria optimization; ordering
remaining options; and a user interface module presenting options to a
user.

[0082] In one embodiment, we have one or more of the following the steps
for the process: choosing an algorithm, ranking entries, calculating
weighted average or weighted sum, applying linear programming, applying
aggregate function, applying constraints or conditions, applying
thresholds, inequality relationships, or equality relationships,
presenting an optimum route for shipping, presenting an optimum route for
traveling, classifying weightage as a percentage number, classifying
weightage as a real number between 0 and 1, highlighting entries on
tables on computer screen, selecting entries on tables on computer
screen, displaying popup menus or windows on computer screen, calculating
rank index, using weightage and rating values, and normalizing values in
a table, using a minimum value, an average value, or a median value.

[0083] In one embodiment, as shown in FIG. 7, the Aggregate Objective
Function (AOF) can be more general than the linear optimization. For
example, it can include polynomial terms, logarithmic, and polylog terms.
FIG. 7 shows an embodiment of an interface.

[0084] Another variation of Table 2 is given below in Table 4, as one
embodiment.

[0085] As shown in Table 4, the first row is check-marked, and the
3rd row is highlighted, with a pop-up menu or window appearing, as
the Appendix to Table 4, e.g. Table 4 being overlapped or covered by the
pop-up menu or window, on screen of the computer, for the user interface
(GUI), originating from the last (on the right side) column of the
3rd row, from the symbol shown on the table.

[0086] The pop-up menu or window shows the phrase (as an example): "Based
on Aggregate Objective Function (AOF)", overlapping Table 4, on the
computer screen, as described above, as an example, for any information
needed for the user.

[0087]FIG. 14 shows a flow chart of a method of an embodiment of the
invention. The decisions are made for "Truck is on time?" and "Is
adjustment automated?" for the flow chart, with options shown as
"automated adjustment" and "manual adjustment", which is followed by
"appointment" and "truck in transit" boxes/steps, which ends up with
"appointment visualize", which goes back to "Truck is on time?" stage or
step, again, as a loop, which can end up as "Destination" for
positive/"Yes" result (for the decision/evaluation box/step), as shown in
FIG. 14.

[0088] Any variations of the above teaching are also intended to be
covered by this patent application.